inst/doc/zonator-results.R

In zonator: Utilities for Zonation Spatial Conservation Prioritization Software

## ----echo = FALSE, message = FALSE------------------------------------------------------------------------------------
hasData <- requireNamespace("zdat", quietly = TRUE) 
if (!hasData) {
  knitr::opts_chunk$set(eval = FALSE)
  msg <- paste("Note: Examples in this vignette require that the", 
               "`zdat` package be installed. The system", 
               "currently running this vignette does not have that package", 
               "installed, so code examples will not be evaluated.",
               "To install `zdat` package, run `install.packages('zdat',",
               "repos='https://jlehtoma.github.io/drat/', type='source')`.")
  msg <- paste(strwrap(msg), collapse = "\n") 
  message(msg)
} 

## ----setup, echo=FALSE------------------------------------------------------------------------------------------------
options(width = 120)

## ----install-zdat, eval=FALSE-----------------------------------------------------------------------------------------
#  install.packages('zdat', repos='https://jlehtoma.github.io/drat/',
#                   type='source')

## ----setup-objects, message=FALSE-------------------------------------------------------------------------------------
library(zonator)

# Start by creating a project using the tutorial data
setup.dir <- system.file("extdata/basic", package = "zdat") 
tutorial.project <- load_zproject(setup.dir)

# Get a specific variant, 01_core_area_zonation
variant.caz <- get_variant(tutorial.project, "01")

# Let's also rename the features and groups while we're at it
featurenames(variant.caz) <- c("Koala", "Masked.owl", "Powerful.owl", 
                             "Tiger.quoll", "Sooty.owl", "Squirrel.glider",
                             "Yellow-bellied.glider")

groupnames(variant.caz) <- c("1" = "mammals", "2" = "owls")

## ----has-results, message=FALSE---------------------------------------------------------------------------------------
has_results(variant.caz)

## ----get-results------------------------------------------------------------------------------------------------------
results.caz <- results(variant.caz)

## ----curves-----------------------------------------------------------------------------------------------------------
# Get feature-specific curves
feature.curves.caz <- curves(results.caz)
head(feature.curves.caz)

# Note! Using curves() variant.caz will work as well
feature.curves.caz <- curves(variant.caz)
head(feature.curves.caz)

# Get just 2 species
duo.curves.caz <- curves(results.caz, cols = c("Koala", "Tiger.quoll"))
head(duo.curves.caz)

## ----grp-curves-------------------------------------------------------------------------------------------------------
# Get group-specific curves
feature.grp.curves.caz <- curves(results.caz, groups = TRUE)
head(feature.grp.curves.caz)

# Get all the aggregate stats for owls
owls.grp.curves.caz <- curves(results.caz, groups = TRUE, 
                              cols = c("min.owls", "mean.owls", "max.owls",
                                     "w.mean.owls", "ext2.owls"))
head(owls.grp.curves.caz)

## ----curves-range-----------------------------------------------------------------------------------------------------

# Get performance values for 2 species for a range between 60% and 75% of 
# landscape lost 
duo.curves.caz.range <- curves(results.caz, cols = c("Koala", "Tiger.quoll"),
                               lost.lower = 0.6, lost.upper = 0.75)
head(duo.curves.caz.range)
tail(duo.curves.caz.range)

## ----perfomance-------------------------------------------------------------------------------------------------------
# Define the levels of landscape lost
lost.levels <- c(0.25, 0.50, 0.75)
# Notice that performance operates with Zresults-object
performance(results.caz, lost.levels)

# Same can be done for groups
performance(results.caz, lost.levels, groups = TRUE)

## ----perfomance-melted------------------------------------------------------------------------------------------------
# Define the levels of landscape lost
lost.levels <- c(0.25, 0.50, 0.75)
# Notice that performance operates with Zresults-object
performance(results.caz, lost.levels, melted = TRUE)

## ----mask-variant-----------------------------------------------------------------------------------------------------
# Variant: 05_hierarchical_removal_mask
variant.mask <- get_variant(tutorial.project, "05")

featurenames(variant.mask) <- c("Koala", "Masked.owl", "Powerful.owl", 
                             "Tiger.quoll", "Sooty.owl", "Squirrel.glider",
                             "Yellow-bellied.glider")
labels <- c("mammals", "owls")
names(labels) <- c(1, 2)
groupnames(variant.mask) <- labels

results.mask <- results(variant.mask)
feature.curves.mask <- curves(results.mask)
feature.grp.curves.mask <- curves(results.mask, groups = TRUE)

## ----curves-plotting-1, fig.width=7, fig.height=5---------------------------------------------------------------------
plot(feature.curves.caz)

## ----curves-plotting-2, fig.width=7, fig.height=5---------------------------------------------------------------------
plot(feature.curves.caz, invert.x = TRUE, main = "All features")

## ----curves-plotting-3, fig.width=7, fig.height=5---------------------------------------------------------------------
plot(feature.curves.caz, invert.x = TRUE, main = "Just few features", 
     subs = c('Yellow-bellied.glider', 'Sooty.owl'), mean = TRUE)

## ----curves-plotting-4, fig.width=7, fig.height=5---------------------------------------------------------------------
plot(feature.grp.curves.caz, invert.x = TRUE, main = "All groups")

## ----curves-plotting-5, fig.width=7, fig.height=5---------------------------------------------------------------------
plot(feature.grp.curves.caz, invert.x = TRUE, main = "All groups", 
     subs = c('owls'))

## ----curves-plotting-6, fig.width=7, fig.height=5---------------------------------------------------------------------
plot(feature.grp.curves.caz, invert.x = TRUE, main = "All groups", 
     monochrome = TRUE)

## ----curves-plotting-7, fig.width=7, fig.height=5---------------------------------------------------------------------
plot(feature.grp.curves.mask, invert.x = TRUE, main = "All groups", 
     mean = TRUE, min = TRUE, max = TRUE)

## ----perfomance-hist, fig.width=7, fig.height=5-----------------------------------------------------------------------
library(ggplot2)
# NOTE: we're using melted=TRUE here
occr.remaining <- performance(results.caz, c(0.9, 0.95, 0.98), melted = TRUE)
ggplot(occr.remaining, aes(x = factor(feature), y = perf.levels,
                           fill = factor(pr.lost))) + 
  geom_bar(position = "dodge", stat = "identity")


## ----rank-raster-1, fig.width=7, fig.height=5-------------------------------------------------------------------------
library(raster)
rank.caz <- rank_raster(results.caz)
# Let's use a spectral color scheme designed for Zonation rank maps
leg <- zlegend("spectral")
plot(rank.caz, breaks = leg$values, col = leg$colors)

## ----rank-raster-2, warning=FALSE, fig.width=7, fig.height=20---------------------------------------------------------
# RasterStack is returned
all.ranks <- rank_rasters(tutorial.project)
plot(all.ranks, breaks = leg$values, col = leg$colors, nc = 1)

## ----rank-raster-3, fig.width=7, fig.height=5-------------------------------------------------------------------------
pairs(stack(all.ranks[[1:3]]), method = "kendall")

## ----rank-raster-4, fig.width=7, fig.height=5-------------------------------------------------------------------------
pas.file <- system.file("extdata/data", "mask_rs.tif", 
                        package = "zdat")
pas <- raster(pas.file)
# Re-class zeros as NA
pas[pas == 0] <- NA
# Plot the histogram of rank.caz within mask raster
plot_hist(x = rank.caz, pas, add.median = TRUE)

## ----rank-rasterVis-1, fig.width=7, fig.height=5, message=FALSE-------------------------------------------------------
library(rasterVis)
# Note that breaks in the following are different from the previous maps
levelplot(rank.caz, FUN.margin = mean, 
          par.settings = rasterTheme(region = leg$colors))